The complexity of modern systems is increasing rapidly and the dominating relationships among system variables have become highly non-linear. This results in difficulty in the identification of a system's operating states. In turn, this difficulty affects the sensitivity of fault detection and imposes a challenge on ensuring the safety of operation. In recent years, Self-Organizing Maps has gained popularity in system monitoring as a robust non-linear dimensionality reduction tool. Self-Organizing Map is able to capture non-linear variations of the system. Therefore, it is sensitive to the change of a system's states leading to early detection of fault. In this paper, a new approach based on Self-Organizing Map is proposed to detect and assess the risk of fault. In addition, probabilistic analysis is applied to characterize the risk of fault into different levels according to the hazard potential to enable a refined monitoring of the system. The proposed approach is applied on two experimental systems. The results from both systems have shown high sensitivity of the proposed approach in detecting and identifying the root cause of faults. The refined monitoring facilitates the determination of the risk of fault and early deployment of remedial actions and safety measures to minimize the potential impact of fault

Item Type:	Refereed Article
Keywords:	Risk Assessment, Fault Detection
Research Division:	Engineering
Research Group:	Chemical engineering
Research Field:	Process control and simulation
Objective Division:	Expanding Knowledge
Objective Group:	Expanding knowledge
Objective Field:	Expanding knowledge in the mathematical sciences
UTAS Author:	Yu, H (Mr Hongyang Yu)
UTAS Author:	Khan, F (Professor Faisal Khan)
UTAS Author:	Garaniya, V (Associate Professor Vikram Garaniya)
ID Code:	99465
Year Published:	2015
Web of Science® Times Cited:	33
Deposited By:	NC Maritime Engineering and Hydrodynamics
Deposited On:	2015-03-25
Last Modified:	2017-11-01
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